Driving electric vehicles' mass adoption: An architecture for the design of human-centric policies to meet climate and societal goals

For a real "green deal"to take place, it is important that technological achievements in the realm of green mobility solutions are paired with novel sustainable and energy efficient mobility models, smart enough to answer the multifaceted needs of their users. Within this challenging context, we set the foundations of a human-centered framework for the analysis and design of policies promoting the mass adoption of electric vehicles (EVs). The proposed data -driven architecture is conceived to leverage the deep intertwining between users' attitudes, mutual influences and technological traits of EVs to support policy makers in studying the effect that individual characteristics and homophily have on the "natural" spread of EVs, and analyzing the costs and benefits of different intervention policies. By introducing the so-called EV-adoptability DNA, compactly representing the individual predisposition towards EVs, the proposed architecture is intended to be an actionable tool to shape a mobility of the future that is centered on the users' needs, aiding in the fight of climate change and the lack of inclusiveness in the green transition. Through extensive simulations carried out by assembling the proposed framework with a set of anonymized real mobility data, we show its potential in supporting the design of policies to foster greener mobility habits and in the analysis of their mid-term effects, even when access to social/personal information is denied.